1. Field of the Invention
This invention relates to monitoring and analyzing complex signals, and in particular, to separating and displaying the independent signal components of complex signals from a signal source.
2. Description of the Related Art
There are many reasons why it is useful to separate specific signals from a mixture of signals. For example, one may wish to separate a single speaker from a mixture of sound signals. In another instance, a physician may wish to isolate and study a single signal from an organ from other signals generated by the same organ. However, separating independent signals that are part of a mixture of signals is a well-studied, but challenging area of signal processing. Typically, the signal sources and their mixing characteristics are unknown. Without knowledge of the signal sources, other than the general assumption that they come from independent sources, the signal processing problem is referred to as “blind separation of sources.” The separation is “blind” because nothing is known about the frequency or phase of the signals from the independent source.
One area that has benefited from signal separation technologies is the area of medical signal processing. For example, since its discovery in the 1920's, the study of electroencephalographic (EEG) data recorded from the human scalp has been frustrated by the fact that each scalp electrode receives the sum of electrical activities taking place in many different parts of the brain, and that separation of these signals is difficult to perform. U.S. Pat. No. 5,383,164, issued to Sejnowski and Bell in 1995, discloses a blind source separation method using the technique of independent component analysis (ICA) and incorporating an information maximizing (“infomax”) principle. This method of separating mixed signals from a plurality of sensors is now commonly referred to as “infomax ICA.” Infomax ICA has been used to process EEG data received with multiple sensors, for example, attached to the scalp, thus allowing separation of electrical signals originating in different unknown signal sources in the brain. By identifying individual signal sources, physicians were then able to identify the physical signal source of the EEG signals, or “effective source” in the brain.
Although infomax ICA has been used to analyze EEG signals, it relies on several idealized assumptions about the underlying signal sources that may not be completely realistic. For example, in infomax ICA, the EEG signal sources are analyzed as if they come from spatially fixed brain locations and have perfectly synchronized activity across the whole domain of brain cortical activity associated with each signal source. In some operational settings, these idealized assumptions may limit the ability to adequately capture underlying complex spatio-temporal dynamics.
Therefore, there is a need for improved methods and systems for analyzing complex signals and for separating component signals from a mixture of signals.